As Trump Administration trade policies generate national and global repercussions, the fundamentals of trade are presented in a new report from the Congressional Research Service to help understand what is happening and what is at stake.
The report explains basic economic concepts, such as why countries trade, it provides data on U.S. trade relationships, and it describes how trade policy is formulated. See U.S. Trade Policy Primer: Frequently Asked Questions, April 2, 2018.
Other new and updated CRS reports include the following.
China-U.S. Trade Issues, updated April 2, 2018
Tricks of the Trade: Section 301 Investigation of Chinese Intellectual Property Practices Concludes (Part I), CRS Legal Sidebar, March 29, 2018
Overview of the Federal Tax System in 2018, March 29, 2018
Afghanistan: Background and U.S. Policy In Brief, updated April 3, 2018
Navy Ford (CVN-78) Class Aircraft Carrier Program: Background and Issues for Congress, updated March 30, 2018
Can Aliens in Immigration Proceedings Be Detained Indefinitely? High Court Rules on Statutory, but not Constitutional Authority, CRS Legal Sidebar, April 3, 2018
District Court Decision May Help Pave the Way for Trump Administration’s Border Wall Plans, CRS Legal Sidebar, April 2, 2018
Americans are paying too much for almost everything, because the United States has long treated its trucking industry as an artifact to be preserved rather than as an opportunity for innovation.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.